SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

Showing 44264450 of 6689 papers

TitleStatusHype
XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework0
Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis0
S2ST: Image-to-Image Translation in the Seed Space of Latent Diffusion0
Using complex prompts to identify fine-grained biases in image generation through ChatGPT-4o0
SafeCFG: Redirecting Harmful Classifier-Free Guidance for Safe Generation0
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations0
Safer Prompts: Reducing IP Risk in Visual Generative AI0
Safe-SD: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking0
Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking0
SafeText: Safe Text-to-image Models via Aligning the Text Encoder0
Safe Text-to-Image Generation: Simply Sanitize the Prompt Embedding0
SafetyDPO: Scalable Safety Alignment for Text-to-Image Generation0
Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction0
Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking0
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation0
A Generative Adversarial Network for AI-Aided Chair Design0
SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills0
Arbitrary Handwriting Image Style Transfer0
Saliency Guided Optimization of Diffusion Latents0
SALSA-TEXT : self attentive latent space based adversarial text generation0
Using GANs to Synthesise Minimum Training Data for Deepfake Generation0
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis0
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model0
Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts0
AGAN: Towards Automated Design of Generative Adversarial Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified